Relative Stability of Random Projection-Based Image Classification

Our aim is to show that randomly generated transformation of high-dimensional data vectors, for example, images, could provide low dimensional features which are stable and suitable for classification tasks. We examine two types of projections: (a) global random projections, i.e., projections of the whole images, and (b) concatenated local projections of spatially-organized parts of an image (for example rectangular image blocks). In both cases, the transformed images provide good features for correct classification. The computational complexity of designing the transformation is linear with respect to the size of images and in case (b) it does not depend on the form of image partition. We have analyzed the stability of classification results with respect to random projection and to different randomly generated training sets. Experiments on the images of ten persons taken from the Extended Yale Database B demonstrate that the methods of classification based on Gaussian random projection are effective and positively comparable with PCA-based methods, both from the point of view of stability and classification accuracy.